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  1. MQL5 Wizard Techniques you should know (Part 17): Multicurrency Trading

    by , 11-04-2024 at 05:52 AM
    This article continues the series on how the MQL5 wizard is ideal for rapid testing and prototyping ideas for traders. For a lot of people developing expert advisers and trade systems, the need to keep learning and be abreast with trends in not just machine learning but trade & risk management in general is important. We therefore consider within these series how the MQL5 IDE is useful in this regard by not only saving time but also minimizing coding errors.
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  2. MQL5 Wizard Techniques you should know (Part 16): Principal Component Analysis with Eigen Vectors

    by , 10-27-2024 at 05:52 AM
    Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts.

    PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that is
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  3. Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies

    by , 09-26-2024 at 04:55 AM
    In the previous article, we started developing a multi-currency EA that works simultaneously with various trading strategies. At the first stage there were only two different strategies. They represented the implementation of the same trading idea, worked on the same trading instrument (symbol) and chart period (timeframe). They differed from each other only in the numerical values of the parameters.

    We are now only interested in testing the suitability of this approach, and
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  4. Market Reactions and Trading Strategies in Response to Dividend Announcements: Evaluating the Efficient Market Hypothesis in Stock Trading

    by , 09-10-2024 at 03:45 PM
    This paper intends to analyze the impact of dividend announcement on the stock market returns that investors earn.
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  5. Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding GCPC)

    by , 08-22-2024 at 07:30 AM
    Goal-Conditioned Behavior Cloning (BC) is a promising approach for solving various offline reinforcement learning problems. Instead of assessing the value of states and actions, BC directly trains the Agent behavior policy, building dependencies between the set goal, the analyzed environment state and the Agent's action. This is achieved using supervised learning methods on pre-collected offline trajectories. The familiar Decision Transformer method and its derivative algorithms have demonstrated
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